Estimation and ModelingSemi-parameter regressionPrecision analysisThe measuring model based on Bi-satellite as well as optical and radar measure was presented; and then the sparse parameter estimation model and the corresponding estimation algorithm and calculation step for exterior ballistic trajectory based...
To overcome this difficulty, we introduce sparse modeling into the estimation of a strain-rate field. Specifically, we impose a sparsity constraint as well as the smoothness constraint on strain rates as prior information, which are expressed by the L1-norm and the L2-norm of the second-order...
,2 are the largest and second largest intensities in the i th cycle, respectively. because 3dec [ 5 ] assigns the called base of i th cycle as the type with the largest intensity, the signal intensities such as x i ,1 and x i ,2 are crucial to the estimation of error probability....
The sparse modeling using logistic regressions not only defines valid Phred scores, but also provides insights into the error mechanism of the sequencing technology by variable selection. Like the AIC and BIC method, the solution toL1-regularized method is sparse and thereby embeds variable selection....
modeling approaches and their connections. We derive the estimation procedures with these priors using variational inference for fully Bayesian estimation. In addition, we discuss the differences between the proposed inference and deterministic inference approaches with these priors. Finally, we show the ...
题目:Sparse LiDAR and Stereo Fusion (SLS-Fusion) for Depth Estimationand 3D Object Detection 名称:用于深度估计和 3D 物体检测的稀疏激光雷达和立体融合 (SLS-Fusion) 论文:arxiv.org/abs/2103.0397 代码: 单位:图卢兹大学 出版:Arxiv 2021 SparseLIF 题目:SparseLIF: High-Performance Sparse LiDAR-Camera ...
A Direct Estimation of High Dimensional Stationary Vector Autoregressions The vector autoregressive (VAR) model is a powerful tool in modeling complex time series and has been exploited in many fields. However, fitting high dimen... F Han,H Lu,H Liu - 《Arxiv Preprint Arxiv》 被引量: 57发...
SPAMS (SPArse Modeling Software) is an optimization toolbox for solving various sparse estimation problems. Dictionary learning and matrix factorization (NMF, sparse PCA, ...) Solving sparse decomposition problems with LARS, coordinate descent, OMP, SOMP, proximal methods ...
2 Sparse representations and clustering 2.1 Sparse representations: An overview Sparse representations have become a key research topic with various applications in signal and data processing, e.g., denoising [36,45], modeling [46], restoration [47,48], compression [49,50], and even more [51...
Sorted L1 Penalized Estimation rsparse-regressionslopegeneralized-linear-models UpdatedMar 14, 2025 C++ Sequential adaptive elastic net (SAEN) approach, complex-valued LARS solver for weighted Lasso/elastic-net problems, and sparsity (or model) order detection with an application to single-snapshot sou...